DBMS > Apache Impala vs. Faircom EDGE vs. Lovefield vs. OrientDB vs. SWC-DB

System Properties Comparison Apache Impala vs. Faircom EDGE vs. Lovefield vs. OrientDB vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Embeddable relational database for web apps written in pure JavaScript

Multi-model DBMS (Document, Graph, Key/Value)

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store

Wide column store

Secondary database models

Document store

Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

impala.apache.org

www.faircom.com/products/faircom-edge

google.github.io/lovefield

orientdb.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

impala.apache.org/impala-docs.html

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

github.com/google/lovefield/blob/master/docs/spec_index.md

www.orientdb.com/docs/last/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

FairCom Corporation

Google

OrientDB LTD; CallidusCloud; SAP

Alex Kashirin

Initial release

2013

1979

2014

2010

2020

Current release

4.1.0, June 2022

V3, October 2020

2.1.12, February 2017

3.2.29, March 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Restricted, free version available

Open Source

Apache 2.0

Open Source

Apache version 2

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

ANSI C, C++

JavaScript

Java

C++

Server operating systems

Linux

Android
Linux

ARM, x86
Raspbian
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

All OS with a Java JDK (>= JDK 6)

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

ANSI SQL queries

SQL-like query language

via JavaScript builder pattern

SQL-like query language, no joins

SQL-like query language

APIs and other access methods

JDBC
ODBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Proprietary protocol
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

JavaScript

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C++

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

.Net, JavaScript, C/C++

Java, Javascript

Triggers

yes

Using read-only observers

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

relationship in graphs

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Access rights for users and roles; record level security configurable

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